SoroushVahidi/scidocs huggingface dataset:
Affordances in Grounded Language Learning
(2018)

We present a novel methodology involving mappings between different modes of semantic representations. We propose distributional semantic models as a mechanism for representing the kind of world knowledge inherent in the system of abstract symbols characteristic of a sophisticated community of language users. Then, motivated by insight from ecological psychology, we describe a model approximating affordances, by which we mean a language learner’s direct perception of opportunities for action in an environment. We present a preliminary experiment involving mapping between these two representational modalities, and propose that our methodology can become the basis for a cognitively inspired model of grounded language learning.
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices
(2018)

A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. The proposed solution employs a novel architecture consisting of wavelet transform and multiple LSTM recurrent neural networks (Fig. 1). Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. The source code is available online [1].
Data Series Management: Fulfilling the Need for Big Sequence Analytics
(2018)

Massive data sequence collections exist in virtually every scientific and social domain, and have to be analyzed to extract useful knowledge. However, no existing data management solution (such as relational databases, column stores, array databases, and time series management systems) can offer native support for sequences and the corresponding operators necessary for complex analytics. We argue for the need to study the theory and foundations for sequence management of big data sequences, and to build corresponding systems that will enable scalable management and analysis of very large sequence collections. To this effect, we need to develop novel techniques to efficiently support a wide range of sequence queries and mining operations, while leveraging modern hardware. The overall goal is to allow analysts across domains to tap in the goldmine of the massive and ever-growing sequence collections they (already) have.
DocChat: An Information Retrieval Approach for Chatbot Engines Using Unstructured Documents
(2016)

Most current chatbot engines are designed to reply to user utterances based on existing utterance-response (or Q-R)1 pairs. In this paper, we present DocChat, a novel information retrieval approach for chatbot engines that can leverage unstructured documents, instead of Q-R pairs, to respond to utterances. A learning to rank model with features designed at different levels of granularity is proposed to measure the relevance between utterances and responses directly. We evaluate our proposed approach in both English and Chinese: (i) For English, we evaluate DocChat on WikiQA and QASent, two answer sentence selection tasks, and compare it with state-of-the-art methods. Reasonable improvements and good adaptability are observed. (ii) For Chinese, we compare DocChat with XiaoIce2, a famous chitchat engine in China, and side-by-side evaluation shows that DocChat is a perfect complement for chatbot engines using Q-R pairs as main source of responses.
An integrated cloud-based platform for labor monitoring and data analysis in precision agriculture
(2013)

Harvest labor has became a prevailing cost in cherry and other Specialty Crops industry. We developed an integrated solution that provided real-time labor monitoring, payroll accrual, and labor-data-based analysis. At the core of our solution is a cloud-based information system that collects labor data from purposely designed labor monitoring devices, and visualizes real-time labor productivity data through a mobile-friendly user interface. Our solution used a proprietary process [1] to accurately associate labor data with its related worker and position under a many-to-many employment relation. We also describe our communication API and protocol, which are specifically designed to improve the reliability of data communication within an orchard. Besides its immediate benefits in improving the efficiency and accuracy of labor monitoring, our solution also enables data analysis and visualization based on harvest labor data. As an example, we discuss our approach of yield mapping based on harvest labor data. We implemented the platform and deployed the system on a cloud-based computing platform for better scalability. An early version of the system has been tested during the 2012 harvest season in cherry orchards in the U.S. Pacific Northwest Region.
PALSAR Radiometric and Geometric Calibration
(2009)

This paper summarizes the results obtained from geometric and radiometric calibrations of the Phased-Array L-Band Synthetic Aperture Radar (PALSAR) on the Advanced Land Observing Satellite, which has been in space for three years. All of the imaging modes of the PALSAR, i.e., single, dual, and full polarimetric strip modes and scanning synthetic aperture radar (SCANSAR), were calibrated and validated using a total of 572 calibration points collected worldwide and distributed targets selected primarily from the Amazon forest. Through raw-data characterization, antenna-pattern estimation using the distributed target data, and polarimetric calibration using the Faraday rotation-free area in the Amazon, we performed the PALSAR radiometric and geometric calibrations and confirmed that the geometric accuracy of the strip mode is 9.7-m root mean square (rms), the geometric accuracy of SCANSAR is 70 m, and the radiometric accuracy is 0.76 dB from a corner-reflector analysis and 0.22 dB from the Amazon data analysis (standard deviation). Polarimetric calibration was successful, resulting in a VV/HH amplitude balance of 1.013 (0.0561 dB) with a standard deviation of 0.062 and a phase balance of 0.612deg with a standard deviation of 2.66deg .
Cascaded seven level inverter with reduced number of switches using level shifting PWM technique
(2013)

A multilevel inverter is a power electronic device that is used for high voltage and high power applications and has many advantages like, low switching stress, low total harmonic distortion (THD). Hence, the size and bulkiness of passive filters can be reduced. This paper proposes two new topologies of a 7-level cascaded multilevel inverter with reduced number of switches than that of conventional type which has 12 switches. The topologies consist of circuits with 9 switches and 7 switches for the same 7-level output. Therefore with less number of switches, there will be a reduction in gate drive circuitry and also very few switches will be conducting for specific intervals of time. The SPWM technique is implemented using multicarrier wave signals. Level Shifted triangular waves are used in comparison with sinusoidal reference to generate Sine PWM switching sequence. The number of level shifted triangular waves depends on the number of levels in the output. i.e. for n levels, n-1 number of carrier waves. This paper uses 1 KHz SPWM pulses with a modulation index of 0.8. The circuits are simulated using SPWM technique and the effect of the harmonic spectrum is analyzed. A comparison is made for the topologies with 9 switches and 7 switches and an effective reduction in THD has been observed for the circuits with less number of switches. The THD for 9 switches is 14% and the THD for 7 switches is 12.5%. The circuits are modeled and simulated with the help of MATLAB/SIMULINK.
Gaze estimation method based on an aspherical model of the cornea: surface of revolution about the optical axis of the eye
(2010)

A novel gaze estimation method based on a novel aspherical model of the cornea is proposed in this paper. The model is a surface of revolution about the optical axis of the eye. The calculation method is explained on the basis of the model. A prototype system for estimating the point of gaze (POG) has been developed using this method. The proposed method has been found to be more accurate than the gaze estimation method based on a spherical model of the cornea.
Evaluation of Multi-view 3D Reconstruction Software
(2015)

A number of software solutions for reconstructing 3D models from multi-view image sets have been released in recent years. Based on an unordered collection of photographs, most of these solutions extract 3D models using structure-from-motion (SFM) algorithms. In this work, we compare the resulting 3D models qualitatively and quantitatively. To achieve these objectives, we have developed different methods of comparison for all software solutions. We discuss the perfomance and existing drawbacks. Particular attention is paid to the ability to create printable 3D models or 3D models usable for other applications.
Beautiful Math, Part 2: Aesthetic Patterns Based on Fractal Tilings
(2014)

A fractal tiling (f-tiling) is a tiling whose boundary is fractal. This article presents two families of rare, infinitely many f-tilings. Each f-tiling is constructed by reducing tiles by a fixed scaling factor, using a single prototile, which is a segment of a regular polygon. The authors designed invariant mappings to automatically produce appealing seamless, colored patterns from such tilings.
The art of deep learning ( applied to NLP )
(2016)

In many regards, tuning deep-learning networks is still more an art than it is a technique. Choosing the correct hyper-parameters and getting a complex network to learn properly can be daunting to people not well versed in that art. Taking the example of entailment classification, this work will analyze different methods and tools that can be used in order to diagnose learning problems. It will aim at providing an deep understanding of some key hyper-parameters, sketch out their effects and explain how to identify recurrent problems when tuning them.
Auditing and Assessment of Data Traffic Flows in an IoT Architecture
(2018)

Recent advances in the development of the Internet of Things and ICT have completely changed the way citizens interact with Smart City environments increasing the demand of more services and infrastructures in many different contexts. Furthermore, citizens require to be active users in a flexible smart living lab, with the possibility to access Smart City data, analyze them, perform actions and receive notifications based on automated decision-making processes. Critical problems could arise if the continuity of data flows and communication among connected IoT devices and data-driven applications is interrupted or lost, due to some devices or system malfunction or unexpected behavior. The proposed solution is a set of instruments, aimed at real-time collecting and storing IoT and Smart City data (data shadow), as well as auditing data traffic flows in an IoT Smart City Architecture, with the purpose of quantitatively monitoring the status and detecting potential anomalies and malfunctions at level of single IoT device and/or service. These instruments are the DevDash and AMMA tools, designed and realized within the Snap4City framework. Specific use cases have been provided to highlight the capabilities of these instruments in terms of data indexing, monitoring and analysis.
Support Vector Machine Active Learning with Applications to Text Classification
(2001)

Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.
A Bayesian hierarchical model for learning natural scene categories
(2005)

We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Each region is represented as part of a "theme". In previous work, such themes were learnt from hand-annotations of experts, while our method learns the theme distributions as well as the codewords distribution over the themes without supervision. We report satisfactory categorization performances on a large set of 13 categories of complex scenes.
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
(2001)

In this paper, we propose a computational model of the recognition of real world scenes that bypasses the segmentation and the processing of individual objects or regions. The procedure is based on a very low dimensional representation of the scene, that we term the Spatial Envelope. We propose a set of perceptual dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the dominant spatial structure of a scene. Then, we show that these dimensions may be reliably estimated using spectral and coarsely localized information. The model generates a multidimensional space in which scenes sharing membership in semantic categories (e.g., streets, highways, coasts) are projected closed together. The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
When two choices are not enough: Balancing at scale in Distributed Stream Processing
(2016)

Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: having more workers implies fewer keys per worker, so it becomes harder to “average out” the cost of hot keys with cold keys. We propose a novel load balancing technique that uses a heavy hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to d ≥ 2 choices to ensure a balanced load, where d is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by 150% and 60% respectively over the previous state-of-the-art when deployed on Apache Storm.
Smart Configuration of Smart Environments
(2016)

One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user's goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains.
Smart Locks: Lessons for Securing Commodity Internet of Things Devices
(2016)

We examine the security of home smart locks: cyber-physical devices that replace traditional door locks with deadbolts that can be electronically controlled by mobile devices or the lock manufacturer's remote servers. We present two categories of attacks against smart locks and analyze the security of five commercially-available locks with respect to these attacks. Our security analysis reveals that flaws in the design, implementation, and interaction models of existing locks can be exploited by several classes of adversaries, allowing them to learn private information about users and gain unauthorized home access. To guide future development of smart locks and similar Internet of Things devices, we propose several defenses that mitigate the attacks we present. One of these defenses is a novel approach to securely and usably communicate a user's intended actions to smart locks, which we prototype and evaluate. Ultimately, our work takes a first step towards illuminating security challenges in the system design and novel functionality introduced by emerging IoT systems.
A hybrid machine learning approach to automatic plant phenotyping for smart agriculture
(2017)

Recently, a new ICT approach to agriculture called “Smart Agriculture” has been received great attention to support farmers' decision-making for good final yield on various kinds of field conditions. For this purpose, this paper presents two image sensing methods that enable an automatic observation to capture flowers and seedpods of soybeans in real fields. The developed image sensing methods are considered as sensors in an agricultural cyber-physical system in which big data on the growth status of agricultural plants and environmental information (e.g., weather, temperature, humidity, solar radiation, soil condition, etc.) are analyzed to mine useful rules for appropriate cultivation. The proposed image sensing methods are constructed by combining several image processing and machine learning techniques. The flower detection is realized based on a coarse-to-fine approach where candidate areas of flowers are first detected by SLIC and hue information, and the acceptance of flowers is decided by CNN. In the seedpod detection, candidates of seedpod regions are first detected by the Viola-Jones object detection method, and we also use CNN to make a final decision on the acceptance of detected seedpods. The performance of the proposed image sensing methods is evaluated for a data set of soybean images that were taken from a crowd of soybeans in real agricultural fields in Hokkaido, Japan.
Low temperature Cu-Cu direct bonding for 3D-IC by using fine crystal layer
(2012)

In this paper, we report a method of low temperature solid diffusion bonding. To investigate bondability of solid diffusion, we examined the effect of bump metals and bump planarization methods. Cu and Au bump were used for bump metals and CMP and ultra-precision cutting were used for bump planarization methods. We found that fine crystal layer could be formed on only cut Cu and Au bumps, and especially cut Cu bumps had a thick fine crystal layer on the surface. The layer on cut Cu bump was found to be easily to recrystallize at low temperature condition of 150 degree C. Moreover, the bonding interface of cut Cu bump disappeared at 200 degree C for 30 min, which means solid diffusion across the interface was realized with the contribution of fine crystal layer. In addition, for Cu-Cu direct bonding, formic acid treatment before bonding is effective because formic acid can react at low temperature without destroying fine crystal layer. That led to achieve high bonding strength between cut Cu bumps.
Hybrid excited claw pole electric machine
(2016)

The paper presents the concept and results of simulation and experimental research of a claw pole generator with hybrid excitation. Hybrid excitation is performed with a conventional coil located between two parts of the claw-shape rotor and additional permanent magnets which are placed on claw poles. Within the research first a simulation and next constructed experimental model has been developed on the basis of the mass-produced vehicle alternator. Experimental researches have shown that - at a suitable rotational speed - it is possible to self-excite of the generator without any additional source of electrical power.
A 92GHz bandwidth SiGe BiCMOS HBT TIA with less than 6dB noise figure
(2015)

A low-noise, broadband amplifier with resistive degeneration and transimpedance feedback is reported with 200 mVpp input linearity and less than 6 dB noise figure up to 88 GHz. The measured gain of 13 dB, noise figure, linearity, and group delay variation of ±1.5 ps are in excellent agreement with simulation. Eye diagram measurements were conducted up to 120 Gb/s and a dynamic range larger than 36 dB was obtained from eye diagram measurements at 40 Gb/s. The chip, which includes a 50Ω output buffer, occupies 0.138 mm2 and consumes 21 mA from a 2.3V supply.
Video Question Answering via Hierarchical Spatio-Temporal Attention Networks
(2017)

Open-ended video question answering is a challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced video content according to the question. However, the existing visual question answering works only focus on the static image, which may be ineffectively applied to video question answering due to the lack of modeling the temporal dynamics of video contents. In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoderdecoder learning framework. We propose the hierarchical spatio-temporal attention network for learning the joint representation of the dynamic video contents according to the given question. We then develop the spatio-temporal attentional encoder-decoder learning method with multi-step reasoning process for open-ended video question answering. We construct a large-scale video question answering dataset. The extensive experiments show the effectiveness of our method.